In this paper, indexing in spatio-temporal databases by using the technique of overlapping is investigated. Overlapping has been previously applied in various access methods to combine consecutive structure instances into a single structure, without storing identical sub-structures. In this way, space is saved without sacrificing time performance. A new access method, overlapping linear quadtrees is introduced. This structure is able to store consecutive historical raster images, a database of evolving images. Moreover, it can be used to support query processing in such a database. Five such spatio-temporal queries along with the respective algorithms that take advantage of the properties of the new structure are introduced. The new access method was implemented and extensive experimental studies for space efficiency and query processing performance were conducted. A number of results of these experiments are presented. As far as space is concerned, these results indicate that, in the case of similar consecutive images, considerable storage is saved in comparison to independent linear quadtrees. In the case of query processing, the results indicate that the proposed algorithmic approaches outperform the respective straightforward algorithms, in most cases. The region data sets used in experiments were real images of meteorological satellite views and synthetic random images with specified aggregation.

Online social networking services have come to dominate the dot com world: Countless online communities coexist on the social Web. Some typically characteristic user attributes, such as gender, age group, sexual orientation, are not automatically part of the profile information. In some cases user attributes can even be deliberately and maliciously falsified. This paper examines automated inference of gender on online social networks by analyzing written text with a combination of natural language processing and classification techniques. Extensive experimentation on LinkedIn and Twitter has yielded accuracy of this gender identification technique of up to 98.4 percent.

Abstract. Research in spatio-temporal databases has largely focused on extensions of access methods for the proper handling of time changing spatial information. In this paper, we present the Multiversion Linear Quadtree (MVLQ), a spatio-temporal access method based on Multiversion B-trees (MVBT) [2], embedding ideas from Linear Region Quadtrees [4]. More specifically, instead of storing independent numerical data having a different transaction-time each, for every consecutive image we store a group of codewords that share the same transaction-time, whereas each codeword represents a spatial subregion. Thus, the new structure may be used as an index mechanism for storing and accessing evolving raster images. We also conducted a thorough experimentation using sequences of real and synthetic raster images. In particular, we examined the time performance of temporal window queries, and provide results for a variety of parameter settings.

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